{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true,
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "# 药品销售数据分析实战"
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "## 数据获取"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             购药时间           社保卡号    商品编码     商品名称 销售数量  应收金额   实收金额\n",
      "0  2018-01-01 星期五      001616528  236701  强力VC银翘片    6  82.8     69\n",
      "1  2018-01-02 星期六      001616528  236701  清热解毒口服液    1    28  24.64\n",
      "2  2018-01-06 星期三     0012602828  236701       感康    2  16.8     15\n",
      "3  2018-01-11 星期一  0010070343428  236701    三九感冒灵    1    28     28\n",
      "4  2018-01-15 星期五    00101554328  236701    三九感冒灵    8   224    208\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "from pandas import Series,DataFrame\n",
    "import pandas as pd\n",
    "#导入数据\n",
    "file_name='./data/朝阳医院2018年销售数据.xlsx'\n",
    "xls = pd.ExcelFile(file_name)\n",
    "dataDF = xls.parse('Sheet1',dtype='object')\n",
    "print(dataDF.head())"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sheet1\n"
     ]
    }
   ],
   "source": [
    "#　使用sheet_names来查看当前表格中包含的所有sheet名称（按顺序）\n",
    "print(xls.sheet_names[0])"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(6578, 7)\n",
      "RangeIndex(start=0, stop=6578, step=1)\n",
      "Index(['购药时间', '社保卡号', '商品编码', '商品名称', '销售数量', '应收金额', '实收金额'], dtype='object')\n",
      "购药时间    6576\n",
      "社保卡号    6576\n",
      "商品编码    6577\n",
      "商品名称    6577\n",
      "销售数量    6577\n",
      "应收金额    6577\n",
      "实收金额    6577\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "#查看基本信息\n",
    "#查看数据几行几列\n",
    "print(dataDF.shape)\n",
    "#查看索引\n",
    "print(dataDF.index)\n",
    "#查看每一列的列表头内容\n",
    "print(dataDF.columns)\n",
    "#查看每一列数据统计数目\n",
    "print(dataDF.count())"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "我们看到总共有6578行 7列 数据，但“购药时间\" 和 \"社保卡号\" 等列存在数据缺失情况\n",
    "\n",
    "后续操作中需要进行相应处理"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## 数据清洗"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "数据清洗过程包括：选择子集、列名重命名、缺失数据处理、数据类型转换、数据排序及异常值处理"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "选择子集 本案例中无需选择子集"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "outputs": [
    {
     "data": {
      "text/plain": "             销售时间           社保卡号    商品编码     商品名称 销售数量  应收金额   实收金额\n0  2018-01-01 星期五      001616528  236701  强力VC银翘片    6  82.8     69\n1  2018-01-02 星期六      001616528  236701  清热解毒口服液    1    28  24.64\n2  2018-01-06 星期三     0012602828  236701       感康    2  16.8     15\n3  2018-01-11 星期一  0010070343428  236701    三九感冒灵    1    28     28\n4  2018-01-15 星期五    00101554328  236701    三九感冒灵    8   224    208",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>销售时间</th>\n      <th>社保卡号</th>\n      <th>商品编码</th>\n      <th>商品名称</th>\n      <th>销售数量</th>\n      <th>应收金额</th>\n      <th>实收金额</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>2018-01-01 星期五</td>\n      <td>001616528</td>\n      <td>236701</td>\n      <td>强力VC银翘片</td>\n      <td>6</td>\n      <td>82.8</td>\n      <td>69</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2018-01-02 星期六</td>\n      <td>001616528</td>\n      <td>236701</td>\n      <td>清热解毒口服液</td>\n      <td>1</td>\n      <td>28</td>\n      <td>24.64</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>2018-01-06 星期三</td>\n      <td>0012602828</td>\n      <td>236701</td>\n      <td>感康</td>\n      <td>2</td>\n      <td>16.8</td>\n      <td>15</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>2018-01-11 星期一</td>\n      <td>0010070343428</td>\n      <td>236701</td>\n      <td>三九感冒灵</td>\n      <td>1</td>\n      <td>28</td>\n      <td>28</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>2018-01-15 星期五</td>\n      <td>00101554328</td>\n      <td>236701</td>\n      <td>三九感冒灵</td>\n      <td>8</td>\n      <td>224</td>\n      <td>208</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#列重命名\n",
    "dataDF.rename(columns={'购药时间':'销售时间'},inplace=True)\n",
    "dataDF.head()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "缺失值处理\n",
    "\n",
    "获取的数据中很有可能存在缺失值，通过查看基本信息可以推测“购药时间”和“社保卡号”这两列存在缺失值，如果不处理这些缺失值会干扰后面的数据分析结果。缺失数据常用的处理方式为删除含有缺失数据的记录或者利用算法去补全缺失数据。在本次案例中为求方便，直接使用dropna函数删除缺失数据"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "删除缺失值前: (6578, 7)\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 6578 entries, 0 to 6577\n",
      "Data columns (total 7 columns):\n",
      " #   Column  Non-Null Count  Dtype \n",
      "---  ------  --------------  ----- \n",
      " 0   销售时间    6576 non-null   object\n",
      " 1   社保卡号    6576 non-null   object\n",
      " 2   商品编码    6577 non-null   object\n",
      " 3   商品名称    6577 non-null   object\n",
      " 4   销售数量    6577 non-null   object\n",
      " 5   应收金额    6577 non-null   object\n",
      " 6   实收金额    6577 non-null   object\n",
      "dtypes: object(7)\n",
      "memory usage: 359.9+ KB\n",
      "None\n",
      "\n",
      "删除缺失值后 (6575, 7)\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 6575 entries, 0 to 6577\n",
      "Data columns (total 7 columns):\n",
      " #   Column  Non-Null Count  Dtype \n",
      "---  ------  --------------  ----- \n",
      " 0   销售时间    6575 non-null   object\n",
      " 1   社保卡号    6575 non-null   object\n",
      " 2   商品编码    6575 non-null   object\n",
      " 3   商品名称    6575 non-null   object\n",
      " 4   销售数量    6575 non-null   object\n",
      " 5   应收金额    6575 non-null   object\n",
      " 6   实收金额    6575 non-null   object\n",
      "dtypes: object(7)\n",
      "memory usage: 410.9+ KB\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "##缺失值处理\n",
    "print('删除缺失值前:', dataDF.shape)\n",
    "\n",
    "# 使用info查看数据信息,\n",
    "print(dataDF.info())\n",
    "#删除缺失值\n",
    "dataDF = dataDF.dropna(subset=['销售时间','社保卡号'], how='any')\n",
    "print('\\n删除缺失值后',dataDF.shape)\n",
    "print(dataDF.info())"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "数据类型转换"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "在导入数据时为了防止导入不进来，会强制所有数据都是object类型，但实际数据分析过程中“销售数量”，“应收金额”，“实收金额”，这些列需要浮点型（float）数据，“销售时间”需要改成时间格式，因此需要对数据类型进行转换。\n",
    "可以使用astype()函数转为浮点型数据："
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "销售时间     object\n",
      "社保卡号     object\n",
      "商品编码     object\n",
      "商品名称     object\n",
      "销售数量    float64\n",
      "应收金额    float64\n",
      "实收金额    float64\n",
      "dtype: object\n"
     ]
    }
   ],
   "source": [
    "#数据类型转换\n",
    "dataDF['销售数量'] = dataDF['销售数量'].astype('float')\n",
    "dataDF['应收金额'] = dataDF['应收金额'].astype('float')\n",
    "dataDF['实收金额'] = dataDF['实收金额'].astype('float')\n",
    "print(dataDF.dtypes)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "我们看到在 销售时间这一列数据中存在星期x这样的数据\n",
    "\n",
    "但在数据分析过程中用不到这样的中文，因此要把销售时间这列中日期和星期使用split函数进行分割\n",
    "\n",
    "分割后的时间，返回的时Series数据类型"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "outputs": [],
   "source": [
    "'''\n",
    "定义函数：分割销售日期，提取销售日期\n",
    "输入：timeColSer 销售时间这一列，Series数据类型，例‘2018-01-01 星期五’\n",
    "输出：分割后的时间，返回Series数据类型，例‘2018-01-01’\n",
    "'''\n",
    "def splitSaletime(timeColSer):\n",
    "    timeList=[]\n",
    "\n",
    "    for value in timeColSer:\n",
    "        dateStr=value.split(' ')[0] #用空格进行分割\n",
    "        timeList.append(dateStr)\n",
    "    timeSer=pd.Series(timeList)    #将列表转行为一维数据Series类型\n",
    "    return timeSer"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "outputs": [
    {
     "data": {
      "text/plain": "         销售时间           社保卡号    商品编码     商品名称  销售数量   应收金额    实收金额\n0  2018-01-01      001616528  236701  强力VC银翘片   6.0   82.8   69.00\n1  2018-01-02      001616528  236701  清热解毒口服液   1.0   28.0   24.64\n2  2018-01-06     0012602828  236701       感康   2.0   16.8   15.00\n3  2018-01-11  0010070343428  236701    三九感冒灵   1.0   28.0   28.00\n4  2018-01-15    00101554328  236701    三九感冒灵   8.0  224.0  208.00",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>销售时间</th>\n      <th>社保卡号</th>\n      <th>商品编码</th>\n      <th>商品名称</th>\n      <th>销售数量</th>\n      <th>应收金额</th>\n      <th>实收金额</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>2018-01-01</td>\n      <td>001616528</td>\n      <td>236701</td>\n      <td>强力VC银翘片</td>\n      <td>6.0</td>\n      <td>82.8</td>\n      <td>69.00</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2018-01-02</td>\n      <td>001616528</td>\n      <td>236701</td>\n      <td>清热解毒口服液</td>\n      <td>1.0</td>\n      <td>28.0</td>\n      <td>24.64</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>2018-01-06</td>\n      <td>0012602828</td>\n      <td>236701</td>\n      <td>感康</td>\n      <td>2.0</td>\n      <td>16.8</td>\n      <td>15.00</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>2018-01-11</td>\n      <td>0010070343428</td>\n      <td>236701</td>\n      <td>三九感冒灵</td>\n      <td>1.0</td>\n      <td>28.0</td>\n      <td>28.00</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>2018-01-15</td>\n      <td>00101554328</td>\n      <td>236701</td>\n      <td>三九感冒灵</td>\n      <td>8.0</td>\n      <td>224.0</td>\n      <td>208.00</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#获取“销售时间”这一列\n",
    "timeSer = dataDF.loc[:,'销售时间']\n",
    "\n",
    "#对字符串进行分割，提取销售日期\n",
    "dateSer = splitSaletime(timeSer)\n",
    "\n",
    "#修改销售时间这一列的值\n",
    "dataDF.loc[:,'销售时间'] = dateSer\n",
    "dataDF.head()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         销售时间           社保卡号    商品编码     商品名称  销售数量   应收金额    实收金额\n",
      "0  2018-01-01      001616528  236701  强力VC银翘片   6.0   82.8   69.00\n",
      "1  2018-01-02      001616528  236701  清热解毒口服液   1.0   28.0   24.64\n",
      "2  2018-01-06     0012602828  236701       感康   2.0   16.8   15.00\n",
      "3  2018-01-11  0010070343428  236701    三九感冒灵   1.0   28.0   28.00\n",
      "4  2018-01-15    00101554328  236701    三九感冒灵   8.0  224.0  208.00\n"
     ]
    }
   ],
   "source": [
    "print(dataDF.head())"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "销售时间    datetime64[ns]\n",
      "社保卡号            object\n",
      "商品编码            object\n",
      "商品名称            object\n",
      "销售数量           float64\n",
      "应收金额           float64\n",
      "实收金额           float64\n",
      "dtype: object\n"
     ]
    }
   ],
   "source": [
    "'''\n",
    "数据类型转换:字符串转换为日期\n",
    "把切割后的日期转为时间格式，方便后面的数据统计:\n",
    "'''\n",
    "#errors='coerce' 如果原始数据不符合日期的格式，转换后的值为空值NaT\n",
    "dataDF.loc[:,'销售时间']=pd.to_datetime(dataDF.loc[:,'销售时间'],format='%Y-%m-%d', errors='coerce')\n",
    "print(dataDF.dtypes)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "outputs": [
    {
     "data": {
      "text/plain": "销售时间    26\n社保卡号     0\n商品编码     0\n商品名称     0\n销售数量     0\n应收金额     0\n实收金额     0\ndtype: int64"
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataDF.isnull().sum()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 6549 entries, 0 to 6573\n",
      "Data columns (total 7 columns):\n",
      " #   Column  Non-Null Count  Dtype         \n",
      "---  ------  --------------  -----         \n",
      " 0   销售时间    6549 non-null   datetime64[ns]\n",
      " 1   社保卡号    6549 non-null   object        \n",
      " 2   商品编码    6549 non-null   object        \n",
      " 3   商品名称    6549 non-null   object        \n",
      " 4   销售数量    6549 non-null   float64       \n",
      " 5   应收金额    6549 non-null   float64       \n",
      " 6   实收金额    6549 non-null   float64       \n",
      "dtypes: datetime64[ns](1), float64(3), object(3)\n",
      "memory usage: 409.3+ KB\n"
     ]
    }
   ],
   "source": [
    "'''\n",
    "转换日期过程中不符合日期格式的数值会被转换为空值\n",
    "删除含有NaT的空行\n",
    "'''\n",
    "dataDF = dataDF.dropna(subset=['销售时间','社保卡号'],how='any')\n",
    "datasDF = dataDF.reset_index(drop = True)\n",
    "dataDF.info()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "数据排序\n",
    "\n",
    "此时时间没有按照顺序排列 我们对它进行排序\n",
    "\n",
    "由于排序之后索引会被打乱 所以也需要重置以下索引\n",
    "\n",
    "其中 by:表示按哪一列进行排序  ascending=True表示升序排列\n",
    "ascending=False  表示降序排列"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "outputs": [
    {
     "data": {
      "text/plain": "        销售时间           社保卡号    商品编码          商品名称  销售数量   应收金额   实收金额\n0 2018-01-01      001616528  236701       强力VC银翘片   6.0   82.8   69.0\n1 2018-01-01     0010616728  865099    硝苯地平片(心痛定)   2.0    3.4    3.0\n2 2018-01-01  0010073966328  861409  非洛地平缓释片(波依定)   5.0  162.5  145.0\n3 2018-01-01  0010073966328  866634   硝苯地平控释片(欣然)   6.0  111.0   92.5\n4 2018-01-01  0010014289328  866851   缬沙坦分散片(易达乐)   1.0   26.0   23.0",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>销售时间</th>\n      <th>社保卡号</th>\n      <th>商品编码</th>\n      <th>商品名称</th>\n      <th>销售数量</th>\n      <th>应收金额</th>\n      <th>实收金额</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>2018-01-01</td>\n      <td>001616528</td>\n      <td>236701</td>\n      <td>强力VC银翘片</td>\n      <td>6.0</td>\n      <td>82.8</td>\n      <td>69.0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2018-01-01</td>\n      <td>0010616728</td>\n      <td>865099</td>\n      <td>硝苯地平片(心痛定)</td>\n      <td>2.0</td>\n      <td>3.4</td>\n      <td>3.0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>2018-01-01</td>\n      <td>0010073966328</td>\n      <td>861409</td>\n      <td>非洛地平缓释片(波依定)</td>\n      <td>5.0</td>\n      <td>162.5</td>\n      <td>145.0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>2018-01-01</td>\n      <td>0010073966328</td>\n      <td>866634</td>\n      <td>硝苯地平控释片(欣然)</td>\n      <td>6.0</td>\n      <td>111.0</td>\n      <td>92.5</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>2018-01-01</td>\n      <td>0010014289328</td>\n      <td>866851</td>\n      <td>缬沙坦分散片(易达乐)</td>\n      <td>1.0</td>\n      <td>26.0</td>\n      <td>23.0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#数据排序\n",
    "dataDF = dataDF.sort_values(by='销售时间', ascending=True)\n",
    "dataDF = dataDF.reset_index(drop=True)\n",
    "dataDF.head()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "异常值处理"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "outputs": [
    {
     "data": {
      "text/plain": "              销售数量         应收金额         实收金额\ncount  6549.000000  6549.000000  6549.000000\nmean      2.384486    50.449076    46.284370\nstd       2.375227    87.696401    81.058426\nmin     -10.000000  -374.000000  -374.000000\n25%       1.000000    14.000000    12.320000\n50%       2.000000    28.000000    26.500000\n75%       2.000000    59.600000    53.000000\nmax      50.000000  2950.000000  2650.000000",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>销售数量</th>\n      <th>应收金额</th>\n      <th>实收金额</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>count</th>\n      <td>6549.000000</td>\n      <td>6549.000000</td>\n      <td>6549.000000</td>\n    </tr>\n    <tr>\n      <th>mean</th>\n      <td>2.384486</td>\n      <td>50.449076</td>\n      <td>46.284370</td>\n    </tr>\n    <tr>\n      <th>std</th>\n      <td>2.375227</td>\n      <td>87.696401</td>\n      <td>81.058426</td>\n    </tr>\n    <tr>\n      <th>min</th>\n      <td>-10.000000</td>\n      <td>-374.000000</td>\n      <td>-374.000000</td>\n    </tr>\n    <tr>\n      <th>25%</th>\n      <td>1.000000</td>\n      <td>14.000000</td>\n      <td>12.320000</td>\n    </tr>\n    <tr>\n      <th>50%</th>\n      <td>2.000000</td>\n      <td>28.000000</td>\n      <td>26.500000</td>\n    </tr>\n    <tr>\n      <th>75%</th>\n      <td>2.000000</td>\n      <td>59.600000</td>\n      <td>53.000000</td>\n    </tr>\n    <tr>\n      <th>max</th>\n      <td>50.000000</td>\n      <td>2950.000000</td>\n      <td>2650.000000</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#查看描述统计信息\n",
    "dataDF.describe()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "通过描述统计信息可以看到，“销售数量”、“应收金额”、“实收金额”这三列数据的最小值出现了负数，这明显不符合常理，数据中存在异常值的干扰，因此要对数据进一步处理，以排除异常值的影响："
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "outputs": [
    {
     "data": {
      "text/plain": "              销售数量         应收金额         实收金额\ncount  6506.000000  6506.000000  6506.000000\nmean      2.405626    50.927897    46.727653\nstd       2.364565    87.650282    80.997726\nmin       1.000000     1.200000     0.030000\n25%       1.000000    14.000000    12.600000\n50%       2.000000    28.000000    27.000000\n75%       2.000000    59.600000    53.000000\nmax      50.000000  2950.000000  2650.000000",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>销售数量</th>\n      <th>应收金额</th>\n      <th>实收金额</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>count</th>\n      <td>6506.000000</td>\n      <td>6506.000000</td>\n      <td>6506.000000</td>\n    </tr>\n    <tr>\n      <th>mean</th>\n      <td>2.405626</td>\n      <td>50.927897</td>\n      <td>46.727653</td>\n    </tr>\n    <tr>\n      <th>std</th>\n      <td>2.364565</td>\n      <td>87.650282</td>\n      <td>80.997726</td>\n    </tr>\n    <tr>\n      <th>min</th>\n      <td>1.000000</td>\n      <td>1.200000</td>\n      <td>0.030000</td>\n    </tr>\n    <tr>\n      <th>25%</th>\n      <td>1.000000</td>\n      <td>14.000000</td>\n      <td>12.600000</td>\n    </tr>\n    <tr>\n      <th>50%</th>\n      <td>2.000000</td>\n      <td>28.000000</td>\n      <td>27.000000</td>\n    </tr>\n    <tr>\n      <th>75%</th>\n      <td>2.000000</td>\n      <td>59.600000</td>\n      <td>53.000000</td>\n    </tr>\n    <tr>\n      <th>max</th>\n      <td>50.000000</td>\n      <td>2950.000000</td>\n      <td>2650.000000</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#将'销售数量'这一列小于0的数据排除掉\n",
    "pop = dataDF.loc[:,'销售数量'] > 0\n",
    "dataDF = dataDF.loc[pop,:]\n",
    "dataDF.describe()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## 构建模型与数据可视化"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "数据清洗完成后们需要利用数据构建模型*也就是计算相应的业务指标），并用可视化的方式呈现结果。"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "(1)业务指标1：月均消费次数\n",
    "\n",
    "月均消费次数=总消费次数/月份数 （同一天内，同一个人所有消费算作一次消费)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "总消费次数= 5342\n",
      "月份数= 6\n",
      "业务指标1：月均消费次数= 890\n"
     ]
    }
   ],
   "source": [
    "#计算总消费次数\n",
    "#删除重复数据\n",
    "kpil_Df = dataDF.drop_duplicates(subset=['销售时间','社保卡号'])\n",
    "totalI = kpil_Df.shape[0]\n",
    "print('总消费次数=',totalI)\n",
    "\n",
    "#计算月份数\n",
    "#按销售时间升序排序\n",
    "kpil_Df = kpil_Df.sort_values(by='销售时间', ascending=True)\n",
    "#重命名行名\n",
    "kpil_Df = kpil_Df.reset_index(drop=True)\n",
    "#获取时间范围\n",
    "startTime = kpil_Df.loc[0,'销售时间']\n",
    "endTime = kpil_Df.loc[totalI-1,'销售时间']\n",
    "#计算月份\n",
    "#天数\n",
    "daysI = (endTime-startTime).days\n",
    "mounthI = daysI//30\n",
    "print('月份数=',mounthI)\n",
    "\n",
    "#月平均消费次数\n",
    "kpil_I = totalI//mounthI\n",
    "print('业务指标1：月均消费次数=', kpil_I)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "业务指标2：月均消费金额= 50668.0\n"
     ]
    }
   ],
   "source": [
    "#指标2 月均消费金额\n",
    "#月均消费金额=总消费金额/月份数\n",
    "#消费总金额\n",
    "totalMoneyF=dataDF.loc[:,'实收金额'].sum()\n",
    "mounthMoney=totalMoneyF//mounthI\n",
    "print('业务指标2：月均消费金额=',mounthMoney)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "业务指标3:客单价= 56.909417821040805\n"
     ]
    }
   ],
   "source": [
    "#指标三 客单价\n",
    "#客单价=总消费金额/总消费次数\n",
    "pct=totalMoneyF/totalI\n",
    "print('业务指标3:客单价=',pct)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## 可视化消费趋势"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                 销售时间           社保卡号    商品编码          商品名称  销售数量   应收金额   实收金额\n",
      "销售时间                                                                          \n",
      "2018-01-01 2018-01-01      001616528  236701       强力VC银翘片   6.0   82.8   69.0\n",
      "2018-01-01 2018-01-01     0010616728  865099    硝苯地平片(心痛定)   2.0    3.4    3.0\n",
      "2018-01-01 2018-01-01  0010073966328  861409  非洛地平缓释片(波依定)   5.0  162.5  145.0\n",
      "2018-01-01 2018-01-01  0010073966328  866634   硝苯地平控释片(欣然)   6.0  111.0   92.5\n",
      "2018-01-01 2018-01-01  0010014289328  866851   缬沙坦分散片(易达乐)   1.0   26.0   23.0\n",
      "<pandas.core.groupby.generic.DataFrameGroupBy object at 0x000001B7C7A4DBA0>\n",
      "             销售数量    应收金额     实收金额\n",
      "销售时间                              \n",
      "2018-01-01   62.0  1375.4  1239.10\n",
      "2018-01-02   29.0   610.2   561.34\n",
      "2018-01-03   43.0  1056.2   962.50\n",
      "2018-01-04   39.0   818.7   766.00\n",
      "2018-01-05  230.0  4658.5  4181.02\n",
      "...           ...     ...      ...\n",
      "2018-07-15  282.0  5570.5  5054.46\n",
      "2018-07-16  101.0  3054.6  2828.10\n",
      "2018-07-17   74.0  1524.2  1420.38\n",
      "2018-07-18   52.0  1005.1   913.00\n",
      "2018-07-19   58.0  1222.3  1120.80\n",
      "\n",
      "[200 rows x 3 columns]\n"
     ]
    },
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#分析每天的消费金额\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib\n",
    "#画图时用于显示中文字符\n",
    "from pylab import mpl\n",
    "mpl.rcParams['font.sans-serif'] = ['SimHei'] # SimHei是黑体的意思\n",
    "#在操作之前先复制一份\n",
    "#mpl.rcParams['font.sans-serif'] = ['Songti'] # SimHei是黑体的意思\n",
    "#font = FontProperties(fname='/Library/Fonts/Songti.ttc') #设置字体\n",
    "#在操作之前先复制一份数据，防止影响清洗后的数据\n",
    "groupDF = dataDF\n",
    "\n",
    "#将销售时间设置为index\n",
    "groupDF.index=groupDF['销售时间']\n",
    "print(groupDF.head())\n",
    "gb=groupDF.groupby(groupDF.index)\n",
    "print(gb)\n",
    "dayDF=gb.sum()\n",
    "print(dayDF)\n",
    "\n",
    "#画图\n",
    "plt.plot(dayDF['实收金额'])\n",
    "plt.title('按天消费金额')\n",
    "plt.xlabel('时间')\n",
    "plt.ylabel('实收金额')\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "从结果可以看出，每天消费总额差异较大，除了个别天出现比较大的消费，大部分人消费情况维持在1000-2000元内"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<pandas.core.groupby.generic.DataFrameGroupBy object at 0x000001B7CA250AC0>\n",
      "        销售数量     应收金额      实收金额\n",
      "销售时间                           \n",
      "1     2527.0  53561.6  49461.19\n",
      "2     1858.0  42028.8  38790.38\n",
      "3     2225.0  45318.0  41597.51\n",
      "4     3005.0  54296.3  48787.84\n",
      "5     2225.0  51263.4  46925.27\n",
      "6     2328.0  52300.8  48327.70\n",
      "7     1483.0  32568.0  30120.22\n"
     ]
    },
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#分析每月的消费金额\n",
    "#先聚合再按月分组进行分析\n",
    "\n",
    "#将销售时间聚合按月分组\n",
    "gb=groupDF.groupby(groupDF.index.month)\n",
    "print(gb)\n",
    "monthDF=gb.sum()\n",
    "print(monthDF)\n",
    "\n",
    "plt.plot(monthDF['实收金额'])\n",
    "plt.title('按月消费金额')\n",
    "plt.xlabel('时间')\n",
    "plt.ylabel('实收金额')\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "结果显示，7月消费金额最少，这是因为7月份的数据不完整，所以不具参考价值。\n",
    "​\n",
    "1月、4月、5月和6月的月消费金额差异不大.\n",
    "​\n",
    "2月和3月的消费金额迅速降低，这可能是2月和3月处于春节期间，大部分人都回家过年的原因。"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "outputs": [
    {
     "data": {
      "text/plain": "                  销售数量\n商品名称                  \n苯磺酸氨氯地平片(安内真)   1781.0\n开博通             1440.0\n酒石酸美托洛尔片(倍他乐克)  1140.0\n硝苯地平片(心痛定)       825.0\n苯磺酸氨氯地平片(络活喜)    796.0",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>销售数量</th>\n    </tr>\n    <tr>\n      <th>商品名称</th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>苯磺酸氨氯地平片(安内真)</th>\n      <td>1781.0</td>\n    </tr>\n    <tr>\n      <th>开博通</th>\n      <td>1440.0</td>\n    </tr>\n    <tr>\n      <th>酒石酸美托洛尔片(倍他乐克)</th>\n      <td>1140.0</td>\n    </tr>\n    <tr>\n      <th>硝苯地平片(心痛定)</th>\n      <td>825.0</td>\n    </tr>\n    <tr>\n      <th>苯磺酸氨氯地平片(络活喜)</th>\n      <td>796.0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#分析药品销售情况\n",
    "#聚合统计各种药品数量\n",
    "medicine = groupDF[['商品名称','销售数量']]\n",
    "bk = medicine.groupby('商品名称')[['销售数量']]\n",
    "re_medicine = bk.sum()\n",
    "\n",
    "\n",
    "#对销售药品数量按将序排序\n",
    "re_medicine = re_medicine.sort_values(by='销售数量', ascending=False)\n",
    "re_medicine.head()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#截取销售数量最多的前十种药品 并用条形图展示结果\n",
    "top_medicine = re_medicine.iloc[:10,:]\n",
    "top_medicine\n",
    "\n",
    "# 数据可视化，用条形图展示前十的药品\n",
    "top_medicine.plot(kind = 'bar')\n",
    "plt.title('销售前十的药品')\n",
    "plt.xlabel('药品')\n",
    "plt.ylabel('数量')\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 每天消售金额 -- 散点图\n",
    "plt.scatter(dataDF['销售时间'],dataDF['实收金额'])\n",
    "plt.title('每天销售金额')\n",
    "plt.xlabel('时间')\n",
    "plt.ylabel('实收金额')\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}